Distance-based social message pruning

ABSTRACT

Correspondences in a social networking system are analyzed to determine at least one topic. An activity stream with the at least one topic is analyzed. A target audience for the activity steam is identified. The activity stream is analyzed according to a uniqueness and a relationship criteria to form an assessment. The assessment is analyzed to a predetermined action criteria. Performing an action responsive to determining the assessment satisfies the predetermined action criteria.

BACKGROUND

The present invention relates generally to the field of data processing,and more particularly to data processing based on a user profile orattribute.

Social media are computer-mediated tools that allow people, companies,and other organizations to create, share, or exchange information,career interests, ideas, and pictures/videos in virtual communities andnetworks. Social media depend on mobile and web-based technologies tocreate highly interactive platforms through which individuals,communities, and organizations can share, co-create, discuss, and modifyuser-generated content. They introduce substantial and pervasive changesto communication between businesses, organizations, communities, andindividuals.

SUMMARY

Embodiments of the present invention disclose a method, computer programproduct, and system for dynamically processing information in anactivity stream based on uniqueness and a relationship criteria.Correspondences in a social networking system are analyzed to determineat least one topic. An activity stream with the at least one topic isanalyzed. A target audience for the activity steam is identified. Theactivity stream is analyzed according to a uniqueness and a relationshipcriteria to form an assessment. The assessment is analyzed to apredetermined action criteria. Performing an action responsive todetermining the assessment satisfies the predetermined action criteria.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 illustrates an operational flowchart illustrating an examplepruning process by a dynamic pruning program according to at least oneembodiment;

FIG. 3 illustrates a close-up view of a portion of an exemplaryknowledge graph for the at least one determined topic and the remainingcorrespondence within a social networking system according to at leastone embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

FIG. 7 illustrates a block diagram of an example natural languageprocessing system configured to analyze content within a socialnetworking system, in accordance with embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Social networking systems may be a universal mechanism to connect endusers and information in logical and organized manners that may enablesharing and processing of information between the end users. Currently,common mechanisms of sharing and processing information utilize aninbox, wall, activity stream, timeline, or profile. Hereinafter, anactivity stream may include, but is not limited to, an instant messaging(IM), short message services (SMS), blog, website, community, news feed,email, Voice over Internet Protocol (VoIP), inbox, wall, timeline, andprofile. These mechanisms enable an end user to rapidly shareinformation with other end users, as well as also gather informationfrom the end users in social networking systems, and have also resultedin a rapid increase in the number of correspondence that must be stored.The number of messages sent per day may be on the order of four to fivebillion emails, tens of millions of photos an hour, a billion messagesper day on social media websites, and five hundred million shortmessages per day. The rapid increase in correspondence results inincredibly large mail files and a personal message data repository whereeach message may be given substantially similar treatment. Thelikelihood two coworkers who are working on the same project from tenyears ago are still corresponding regarding the past project may be veryunlikely. Accordingly, there is a clear value to selectively pruningcorrespondence within a social networking system. Hereafter, pruningrefers to deleting, removing, separating, merging, and/or archivingcorrespondence(s) within a social networking system.

Therefore, it may be advantageous to, among other things, provide a wayto dynamically parse correspondence(s) in a social networkingenvironment, and then determine which portion of the parsedcorrespondence(s) to prune. The correspondence(s) may be pruned byselecting a primary population of messages within the social network,determining a unique set of attributes for the correspondence(s) of theprimary population, analyzing the correspondence(s) of the primarypopulation, establishing generation links between the correspondence(s),and then suggest conversations to be pruned based, at least in part, onthe number of established generation links.

A pruning program may be implemented to assist an end user with pruningcorrespondence(s) within a social network (e.g., a computingenvironment) by automatically establishing links betweencorrespondence(s) (e.g., messages or text on a wall) within the socialnetworking system. The end user may be a user of the social networkingsystem. The end user may have every message since she joined the socialnetworking system in 2013 (e.g., 120,000 messages in three yearscollected at a hundred messages a day that amounts to five gigabytesworth of messages). A message administrator of the social networkingsystem may have set a limit of 100,000 messages for the end user. As theend user has 120,000 messages, a threshold relating to the message limitmay have been satisfied (e.g., the number of messages saved for the userhas exceeded the limit), and the pruning program may be activated inresponse to the threshold being satisfied. The pruning program maydisplay a prompt for the end user to select her most important messagesuntil she selects a predetermined amount of messages (e.g., 100messages). The pruning program may group each user selected message intoa conversation data element, and then may analyze attributes for theconversation data elements (e.g., users, subject, and natural language).The pruning program may then identify an activity stream (e.g.,remaining messages within an inbox), and then analyze the remainingmessages for attributes (e.g., users, subject, and natural language)analyzed within and related to the user selected messages. The pruningprogram may then establish a generation link with the user selectedmessages to the remaining (e.g., unselected) messages within theactivity stream for each overlap in the attributes (e.g., users,subject, and natural language) that may be in the form of a link betweentwo nodes, and a weight of one may be added for each link between a userselected message and an unselected message. The pruning program maysuggest a portion of the messages within the social messaging system toprune based on the unlinked correspondence(s) elements. The end user mayend up reducing the total messages in her activity stream based on thepruning. The end user may have selected these based on a tree or a graphrepresenting the relationships between conversations that may have beengenerated and then displayed by the pruning program.

The following described exemplary embodiments provide a system, method,and program product for dynamically pruning social networking systemsbased on keywords and analyzed data elements within user selectedelectronic document (e.g., messages) within the social networkingsystem. As such, embodiments of the present disclosure may improve thetechnical field of data processing by organizing/pruning correspondencewithin a social networking system according to a linkage betweencorrespondences. More specifically, embodiments may reduce irrelevantcorrespondences within a social networking system by dynamically pruningcorrespondences according to content and/or keywords within userselected messages so that a user may reduce wasting resources.

It is to be understood that the aforementioned advantages are exampleadvantages and should not be construed as limiting. Embodiments of thepresent disclosure can contain all, some, or none of the aforementionedadvantages while remaining within the spirit and scope of the presentdisclosure.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with at least one embodiment is depicted. The networkedcomputer environment 100 may include a computer 102 with a processor 104and a data storage device 106 that is enabled to run a software program108 and a dynamic pruning program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run adynamic pruning program 110 b that may interact with a database 114 anda communication network 116. The networked computer environment 100 mayinclude one or more computers 102 and servers 112, only one of which isshown. The communication network 116 may include various types ofcommunication networks, such as a wide area network (WAN), local areanetwork (LAN), a telecommunication network, a wireless network, a publicswitched network and/or a satellite network. It should be appreciatedthat FIG. 1 provides only an illustration of one implementation and doesnot imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the dynamic pruning program110 a, 110 b may interact with a database 114 that may be embedded invarious storage devices, such as, but not limited to a computer/mobiledevice 102, a networked server 112, or a cloud storage service. Thedatabase 114 can include a repository of any transactions associated orinitiated with the dynamic pruning program 110 a and 110 b. The dynamicpruning program 110 a and 110 b may be updated in any system associatedwith the dynamic pruning program 110 a and 110 b (e.g., database 114).

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the dynamic pruning program 110 a, 110b (respectively) to parse correspondence within a social network system,analyze any activity stream associated with the social networking systemaccording to a uniqueness and/or a relationship criteria to form anassessment, then prune, among other actions, a portion ofcorrespondence(s) associated with the social networking system. Thedynamic pruning method is explained in more detail below with respect toFIGS. 2 and 3.

Referring now to FIG. 2, an operational flowchart illustrating theexemplary pruning process 200 (e.g., executed by the dynamic pruningprogram 110 a and 110 b shown in FIG. 1) according to at least oneembodiment is depicted. At 202, the dynamic pruning program 110 a and110 b (FIG. 1) transmits a request to analyze correspondences in asocial networking system to determine at least one topic. The request toanalyze correspondences in a social networking system to determine atleast one topic may activate the dynamic pruning program 110 a and 110 b(FIG. 1). The request may have been an automatic setting made by theuser as an option within the dynamic pruning program 110 a and 110 b(FIG. 1). For example, the dynamic pruning program 110 a and 110 b(FIG. 1) may have an option to automatically transmit the request whencorrespondence(s) associated with the social networking system satisfiesa threshold (e.g., an inbox associated with a user of the socialnetworking system contains more than five thousand messages). Thedynamic pruning program 110 a and 110 b (FIG. 1) may display the requestwithin a user interface (UI).

At 204, the dynamic pruning program 110 a and 110 b (FIG. 1) determinesthe at least one topic. The at least one topic may be one or messages orelectronic documents that each may include attribute within text. Thedynamic pruning program 110 a and 110 b (FIG. 1) may determine the atleast one topic by requesting a user to select the at least one topicusing a generated list compiled by the dynamic pruning program 110 a and110 b (FIG. 1) that may be displayed within the UI. The at least onetopic may be a set of messages selected by the user as anchor points foranalysis. The displayed list may be all correspondence within the socialnetworking system, and the user may select a predetermined amount ofmessages (e.g., one hundred messages) from the social networking systemthat the user may define as important or include content (e.g., events,projects, subject material, confidential information, etc.) that hethinks is important. The at least one determined topic may be at least asingle message selected by the user, up to a predefined threshold (e.g.,5 messages or 5,000 messages), or the user may select as many messagesas he wishes. The dynamic pruning program 110 a and 110 b (FIG. 1) mayalso automatically determine the at least one topic based on historicaldata that includes previous selections by the user. The dynamic pruningprogram 110 a and 110 b (FIG. 1) may utilize analytical softwaretechniques commonly known in the art to generate the list of messages,or to determine the at least one topic. In some embodiments, the dynamicpruning program 110 a and 110 b (FIG. 1) may automatically select the atleast one topic from a last hour, week, month, and/or year of activitywithin the social networking system, or may be selected based on auser's frequently used search terms or topics.

Next, at 206, the dynamic pruning program 110 a and 110 b (FIG. 1)associates an activity stream with the at least one determined topic.For example, if the at least one determined topic is a message within aninbox of a social networking system, then the activity stream may be anaggregate of the remaining messages within the inbox. The activitystream may be an aggregate of all correspondence within the socialnetworking system, or any text written or displayed within a userprofile of the social networking system. For example, the activitystream may be an aggregate of all messages (inbox, sent, spam, drafting,trash, etc.) associated with the social networking system, and may alsobe any text or messages associated with a second social networkingsystem that is also associated with the social networking system.

Then, at 208, the dynamic pruning program 110 a and 110 b (FIG. 1)identifies a target audience for the activity stream. The targetaudience may be correspondence within the social networking system withsubstantially similar or related content as the at least one determinedtopic. For example, the target audience may have the same sender orreceiver of a selected message, or include events or projects that areincluded in the at least one determined topic. The dynamic pruningprogram 110 a and 110 b (FIG. 1) may identify the target audience byparsing the at least one determined topic (e.g., the user selectedmessages), then generate corresponding data structures for one or moreportions of the user selected messages. For example, the dynamic pruningprogram 110 a and 110 b (FIG. 1) may output parsed text elements fromthe user selected messages as data structures. Additionally, a parsedtext element may be represented in the form of a parse tree or othergraph structure. The dynamic pruning program 110 a and 110 b (FIG. 1)may also parse audio and video recordings within the user selectedmessages. The target audience may be any messages within an inbox of asocial networking system that includes the parsed text elements orkeywords that are associated with keywords or parsed text elements ofthe at least one determined topic.

Additionally, the dynamic pruning program 110 a and 110 b (FIG. 1) maygenerate then extract a correspondence identification from the at leastone determined topic, then query a data repository (e.g., database 114)for all the text elements related to the conversation identification.The dynamic pruning program 110 a and 110 b (FIG. 1) may linkcorrespondence text elements based on substantially similar attributes.The dynamic pruning program 110 a and 110 b (FIG. 1) may create asynthetic message representing one or more users, addresses, key terms,n-grams, natural language, and metadata. The synthetic message may be amessage compiled by the dynamic pruning program 110 a and 110 b (FIG. 1)that includes keywords and attributes of the at least one determinedtopic. The dynamic pruning program 110 a and 110 b (FIG. 1) may extractdata from within the synthetic message using natural language processingand field access via application program interface (API) to thesynthetic message, and may ignore signatures or greetings within theactivity stream when processing the natural language. The dynamicpruning program 110 a and 110 b (FIG. 1) may keep a synthetic element ofthe synthetic message, such as { “conversation”: { subject: Fred'sRetirement, users: {fred, bob, alice, charlie} } }. Additionally, thedynamic pruning program 110 a and 110 b (FIG. 1) may ignore key terms orattributes (e.g., tea, finance, etc.); and, may also decay theimportance of specific terms (e.g. tea is never important, or finance isonly important for a finite time); and, may ignore repeated messages orwords, such as the words “thanks” and/or “hello,” or duplicate messages.The dynamic pruning program 110 a and 110 b (FIG. 1) may ignore terms,or messages, and then store those matching messages in a separate datarepository.

According to at least one embodiment, the dynamic pruning program 110 aand 110 b (FIG. 1) may be or include a natural language processingsystem capable of executing entity resolution techniques that may behelpful in identifying important entities within the user selectedmessages. Entity resolution techniques may identify concepts andkeywords within a user selected message. Once entities have beenidentified, correlations and linguistic links between entities may bedetected and used to establish relevance of the entities and,ultimately, the context of the user selected messages. An exampletechnique that may be useful in determining the relative importance of agiven entity to the context of the passage is inverse documentfrequency, which utilizes the relative commonality of the entity as anindicator of its importance to evaluating context. Many other techniquesmay also be used. These same techniques may be useful for determiningthe main idea or critical words of the user selected messages and thenidentifying a target audience (e.g., remaining messages within theactivity stream that include a substantially similar main idea orkeyword).

The text elements may be any words in the form of text, audio, or videothat appears more than once or have a relative importance to the userselected messages (e.g., the message date, title, sender, receiver,and/or frequently used words). The text elements may be a concept withinthe text of the user selected messages or within an audio and videorecording within the user selected messages. The parsed text elements orkeywords of the user selected messages may be included more than onceand may be a different font (e.g., larger than other words within theuser selected messages) or presented in a different manner than otherwords within the user selected messages (e.g., bolded or in italics).Additionally, the text element and/or keywords may be listed in a tablefor visual view to the end user. The table may be ordered based on userpre-configuration (e.g., most important to least important).

According to some embodiments, the dynamic pruning program 110 a and 110b (FIG. 1) may generate a knowledge graph for the user selectedmessages. The knowledge graph may have the same or similarcharacteristics and/or appearance as the knowledge graph that will bediscussed in reference to FIG. 3. For example, the knowledge graph mayinclude a plurality of nodes and edges. The nodes may relate to conceptsfound in the user selected messages, such as keywords, message titles,and/or who is receiving/transmitting the correspondence. The nodes maybe linked together with edges to represent a connection between thenodes. The knowledge graph will be discussed in further detail withreference to FIG. 3.

Then, at 210, the dynamic pruning program 110 a and 110 b (FIG. 1)analyzes the activity stream according to a uniqueness (e.g., agenerated numerical value) and a relationship criteria to form anassessment. The uniqueness may be associated with links of the knowledgegraph that connect two nodes. The links may have a metric “distance”that may define the uniqueness (e.g., quantity of connections) and therelationship (e.g., the type of connection) between two nodes that areconnected by the link. The types of connections may be based ongeneration (e.g., a measure of relatedness), conversation subject,natural language, and/or addressees. The relationship criteria betweenany two nodes may be based on an elapsed time between the nodes (e.g.,frequency of correspondence or date since last correspondence), acommunity distinction (e.g., a member of a rock climbing communitycommunicating with a member of a skydiving community), and/or a socialrelationship between the target audience and the at least one determinedtopic (e.g., correspondence between a manager and an assistant). Thedynamic pruning program 110 a and 110 b (FIG. 1) may analyze theactivity stream in multiple iterations, each iteration analyzing theactivity stream according to a disparate synthetic message that includesone or more key terms related to the at least one determined topic. Theassessment may be in the form of a grouped activity stream that includesthe selected messages, target audience, and the remaining messages thatare organized into four groups: a first generation (i.e., the at leastone determined topic); a second generation (i.e., the target audience);a third generation (i.e., a target audience of the target audience) thatmay be messages that may include substantially similar content as thetarget audience; and, an orphan generation that may be remainingmessages of the activity stream (i.e., unlinked messages within theknowledge graph) which are neither the at least one determined topic(e.g., user selected messages) nor the target audience. The firstgeneration, second generation, and third generation may all be stored ina same data repository (e.g., database 114) or in disparate datarepositories, or some combination thereof. In some embodiments, thedynamic pruning program 110 a and 110 b (FIG. 1) may establish a datamanagement policy that may comply with the following conditions: thethird generation is stored on a tape, the second generation is stored ona disk, and the first generation may be stored in a solid-state drive,or some combination thereof.

The dynamic pruning program 110 a and 110 b (FIG. 1) may, in part, formthe assessment by weighting links (e.g., each link has a weight of one)of the knowledge graph, as well as parsed text elements and/or keywordswithin the at least one determined topic and/or target audience.Weighting may occur in instances when there are multiple identified textelements and/or keywords that are not relevant but appear multiple timeswithin the target audience. For example, if the unrelated text elementsand/or keywords are “surf” and “skyscraper,” and the unrelateddetermined topics appear multiple times throughout the user selectedmessages, each unrelated determined topic may be weighted to determinewhich topic more accurately describes the content of the correspondenceand/or target audience within the social networking system for pruning.The dynamic pruning program 110 a and 110 b (FIG. 1) may weight eachtext element and/or keyword according to the number of appearanceswithin the user selected messages or according to the location withinthe user selected messages (e.g., in the title). The value of theweights given to the text elements and/or keywords may be adjusted bythe user or automatically by the dynamic pruning program 110 a and 110 b(FIG. 1).

Then, at 212, the dynamic pruning program 110 a and 110 b (FIG. 1)compares the assessment to a predetermined action criteria. Thepredetermined action criteria may be a user defined policy or anautomated policy implemented by dynamic pruning program 110 a and 110 b(FIG. 1) that prunes a portion of the activity stream according to aparticular generation. For example, the predetermined action criteriamay prune any correspondence that is more than two generations (i.e.,prune the third generation, and orphan generation); prune allgenerations besides the first generation; or prune the orphangeneration, and store the third generation to a solid-state drive ortape; or, prune and then store all generations besides the firstgeneration to separate data repositories. In some embodiments, thedynamic pruning program 110 a and 110 b (FIG. 1) may determine theaction criteria after no selection has occurred for a predefined amountof time (e.g., five minutes).

Then, at 214, the dynamic pruning program 110 a and 110 b (FIG. 1)determines whether the assessment satisfies the predetermined actioncriteria based on the comparison. If the dynamic pruning program 110 aand 110 b (FIG. 1) determines the assessment satisfies the predeterminedaction criteria based on the comparison (214, “YES” branch), the pruningprocess 200 may perform an action at 216. If the dynamic pruning program110 a and 110 b (FIG. 1) determines the assessment does not satisfy thepredetermined action criteria based on the comparison (214, “NO”branch), the pruning process 200 may continue to receive a userselection at 218.

If the dynamic pruning program 110 a and 110 b (FIG. 1) determines theassessment satisfies the predetermined action criteria based on thecomparison, then, at 216, the dynamic pruning program 110 a and 110 b(FIG. 1) performs an action. The dynamic pruning program 110 a and 110 b(FIG. 1) may prune each message that does not satisfy the assessment.For example, the dynamic pruning program 110 a and 110 b (FIG. 1) mayprune the messages with zero linkage with other messages (i.e., orphangeneration messages), or weak links (i.e., third generation messages).The user may preconfigure the dynamic pruning program 110 a and 110 b(FIG. 1) to display the messages to be pruned so that the user maydetermine if any messages should not be deleted. The dynamic pruningprogram 110 a and 110 b (FIG. 1) may also store backup copies of anypruned messages according to the management policy. Alternatively, theperformed action may include presenting an organizational update of theactivity stream (e.g., a merging, a separation, or an archive). Themerging may include grouping substantially similar correspondence intoone or more folders within the social networking system based on thegeneration of the correspondence. The separation may include separatingcorrespondences that includes disparate content into one or more foldersbased on the content or the generation of the correspondence. Thearchiving may include storing correspondence into one or more databases(e.g., database 114) based on the content or the generation of thecorrespondence. Once the dynamic pruning program 110 a and 110 b(FIG. 1) performs an action, the pruning process 200 may terminate.

However, if the dynamic pruning program 110 a and 110 b (FIG. 1)determines the assessment does not satisfy the predetermined actioncriteria based on the comparison, then, at 218, the dynamic pruningprogram 110 a and 110 b (FIG. 1) displays a selection for the user toreturn to 202. If the dynamic pruning program 110 a and 110 b (FIG. 1)receives a selection from the user to return to 202 (218, “YES” branch),the pruning process 200 may return to 202. If the dynamic pruningprogram 110 a and 110 b (FIG. 1) receives a selection from the user tonot return to 202, (218, “NO” branch), the pruning process 200 mayterminate.

According to at least one embodiment, the dynamic pruning program 110 aand 110 b (FIG. 1) may selectively ignore messages (e.g., all messagesfrom management, all messages with the word tea or bank, and/or allpayroll stubs) based on historical data, and may also ignore messageswhich need to follow a message retention policy (e.g., legal messages).The dynamic pruning program 110 a and 110 b (FIG. 1) may establish aminimum retention policy for a message that is identified as an orphangeneration, and then may present a report of the orphaned messages, andthe date at which the orphaned generations are to be removed. Thedynamic pruning program 110 a and 110 b (FIG. 1) may predict that amessage is going to be an orphan generation due to the reduction in thelinks/strengths of the links between nodes of the knowledge graph,discussed more with reference to FIG. 3. The dynamic pruning program 110a and 110 b (FIG. 1) may monitor search terms to record matches betweenthe terms and possible orphaned data. The dynamic pruning program 110 aand 110 b (FIG. 1) may include linked data such as files, wikis, blogs,and/or activity streams. As previously discussed, activity streams mayinclude files, wikis, blogs, email correspondence, and any other textand/or correspondence associated with a social networking system. Thedynamic pruning program 110 a and 110 b (FIG. 1) may store results fromprior analysis to reduce repeated calculation.

According to at least one embodiment, a user may select one or moreemails within a social networking system to store. A processor mayidentify key terms/concepts within the one or more user selected messageusing NLP. The processor may weigh the key terms/concepts according toimportance (e.g., number of times the email was used, where the emailwas used, similar subject of email, and/or sender of the email). Theprocessor may score each unselected email according to the overlap ofkey concepts (i.e., the number of times the key concepts are in eachunselected email). The processor may rank the unselected emails. Theprocessor may allow user (or automatically) to prune the set ofunselected emails according to their score and the maximum number ofmessages allowed.

FIG. 3 illustrates a close-up view of a portion 300A of an exemplaryknowledge graph 300 for the at least one determined topic and theremaining correspondence within a social networking system, inaccordance with embodiments of the present disclosure. The close-up viewof the portion 300A includes eleven nodes 301-311, with each noderepresenting a different concept. For example, a node may represent atitle, addressee, event, key word, and/or main idea of a parsedcorrespondence according to a uniqueness and a relationship criteria.For example, a node may represent an at least one determined topic, or acorrespondence within the activity stream. The nodes 301-311 areconnected by edges that represent connections between thecorrespondences. For example, if two connected correspondencescorrespond to an event or an ongoing project, an edge connecting themmay represent a link for the event or the project. There may be twolinks connecting them, a first link representing the event, and a secondlink representing the project. The dynamic pruning program 110 a and 110b (FIG. 1) may generate the knowledge graph 300 using known naturallanguage processing techniques. The illustrated portion 300A of theknowledge graph 300 is an undirected part of the knowledge graph,meaning that the edges shown represent symmetric relations between theconcepts. If, however, an edge presented a different relationship, theedge may be a directed edge.

The number of edges connecting two concepts may correspond to a level ofrelatedness between the concepts. For example, concept A 301, which maycorrespond to an at least one determined topic (e.g., a first message),and concept B 302, which may correspond to a correspondence within theactivity stream (e.g., a second message), are connected with threeedges, whereas concept A 301 is connected to concept E 305, which maycorrespond to a similar sender of correspondence, by a single edge. Thismay indicate that concept A 301 and concept B 302 are more closelyrelated than concept A 301 and concept E 305. As an additional example,concept C 303 may correspond to correspondence within an inbox folder(e.g., the activity stream) and concept G 307 may correspond tocorrespondence within a sent folder (e.g., the activity stream), and areconnected with two edges. The two edges between concept C 303 andconcept G 307 may represent a title of a message and a key term. Thedynamic pruning program 110 a and 110 b (FIG. 1) may assign a numericalvalue for two concepts based on the number of edges connecting the twoconcepts together.

The numerical value may also consider the relatedness of concepts that,while not directly connected to each other in the knowledge graph 300,are each connected to the same concept. The dynamic pruning program 110a and 110 b (FIG. 1) may look at whether an event or key term linkingtwo edges can be taken through other concepts to connect the twoconcepts. For example, an event can be drawn to connect concept A 301and concept F 306 by going through concept E 305, which may correspondto a project that is included in both concept A 310 and concept F 306.The length of the path may be considered when determining a numericalvalue (i.e., uniqueness and a relationship criteria) between twoconcepts. For example, the numerical value may be based on the degreesof separation between concepts. Two concepts that are linked together(e.g., concept A 301 and concept B 302) may have 1 degree of separation,whereas two concepts that are not linked together but are both linked toa third concept (e.g., concept A 301 and concept F 306) may have 2degrees of separation, for example. Additionally, the numerical valuecan be inversely related to the number of degrees of separation.

The dynamic pruning program 110 a and 110 b (FIG. 1) may also considerthe number of other concepts that the two concepts are connected to indetermining a numerical value. For example, concept G 307 is notconnected by an edge to concept A 301. However, concept G 307 andconcept A 301 are both connected to concepts C 303 and B 302. Thedynamic pruning program 110 a and 110 b (FIG. 1) may determine that,despite not being directly connected, concepts G 307 and A 301 aresomewhat related. Accordingly, the numerical value between concepts G307 and A 301 may be higher than the numerical value between concept A301 and concept I 309, which are distantly connected to each other, orthan concept A 301 and concept K 311, which cannot be connected.

The illustrated portion 300A of the knowledge graph 300 has twoconnected components. A connected component of an undirected graphincludes a subgraph in which any two nodes in the subgraph are connectedto each other by paths (including paths through other nodes), but cannotbe connected to at least one other node in the graph. For example,concept K 311 and concept J 310 are connected to each other, but no pathexists in the illustrated portion 300A of the knowledge graph 300 thatcan connect either concept K 311 or concept J 310 to concept I 309.Likewise, any two nodes that represent concepts A through I 301-309 canbe connected to each other by at least one path, but none of the nodesrepresenting concepts A through I 301-309 can be connected to eitherconcept J 310 or concept K 311. Because there are two subgraphs thatsatisfy this criteria, the illustrated portion 300A of the knowledgegraph 300 includes two connected components.

The knowledge graph 300 (or a portion thereof) may have an isolated node(i.e., an orphan generation). An isolated node includes a node relatingto a concept that does not connect to any other nodes through an edge.Isolated nodes (i.e., an orphan generation) may be particularly likelyto exist in knowledge graphs generated for correspondences (e.g., atleast one determined topic, message of the activity stream, and/orsynthetic message) mentioned only briefly (e.g., in a single message).An isolated node is a type of connected component.

The nodes 301-311 may be generated using “fuzzy logic” and/or conceptmatching, which may be done to ensure that different words or phrasesrelating to the same concept are included in a single node (e.g., if anevent's title changes throughout an activity stream). Fuzzy logic is atechnique that may represent different representations of an event orconcept as a same entity. For example, the at least one determined topicmay refer to an event's “title,” “Ceremony,” and “Banquet” at differentpoints. The dynamic pruning program 110 a and 110 b (FIG. 1) usingnatural language processing techniques and fuzzy logic may determinethat all three words refer to the same concept. Accordingly, all threeterms may be represented in the knowledge graph using a single node andany edges between any of the three terms and other concepts may connectto that node.

The nodes 301-311 can be weighted according to their importance. Thismay be represented in the knowledge graph 300 by making the nodes301-311 larger or smaller. The nodes 301-311 may be weighted accordingto the number of edges that connect to the nodes. The nodes 301-311 maybe weighted according to the importance of the associated concept. Forexample, correspondences within the activity stream that include animportant project may be weighted more than concepts relating to events(e.g., lunch and/or party). Also, at least one topic previously used bythe user may be weighted more heavily.

One or more of the nodes 301-311 may be considered potentially importantnodes. This may be represented in the knowledge graph by making thepotentially important nodes larger, smaller, or boldface type. A nodemay be a potentially important node if it has a high number of edgesconnecting to it. For example, the dynamic pruning program 110 a and 110b (FIG. 1) may determine that a node is a potentially important node bycomparing the number of edges connected to the node to an important nodethreshold. The important node threshold may be configured by a user. Theimportant node threshold may be determined by the dynamic pruningprogram 110 a and 110 b (FIG. 1) based on the number of edges connectedto each node. For example, the dynamic pruning program 110 a and 110 b(FIG. 1) may determine that 10% of nodes in the knowledge graph havemore than 20 edges connected to them. Accordingly, the dynamic pruningprogram 110 a and 110 b (FIG. 1) may set the important node threshold at20 edges. Therefore, any node with more than 20 connected edges may beconsidered a potentially important node.

It may be appreciated that FIGS. 2 and 3 provide only an illustration ofone embodiment and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902 a, b, and 904, b is representative of anyelectronic device capable of executing machine-readable programinstructions. Data processing system 902 a, b, and 904, b may berepresentative of a smart phone, a computer system, PDA, or otherelectronic devices. Examples of computing systems, environments, and/orconfigurations that may represented by data processing system 902 a, b,and, 904 a, b include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,network PCs, minicomputer systems, and distributed cloud computingenvironments that include any of the above systems or devices.

User client computer 102 (FIG. 1), and network server 112 (FIG. 1) mayinclude respective sets of internal components 902 a, b and externalcomponents 904 a, b illustrated in FIG. 4. Each of the sets of internalcomponents 902 a, b includes one or more processors 906, one or morecomputer-readable RAMs 908 and one or more computer-readable ROMs 910 onone or more buses 912, and one or more operating systems 914 and one ormore computer-readable tangible storage devices 916. The one or moreoperating systems 914 and the software program 108 (FIG. 1) and thedynamic pruning program 110 a (FIG. 1) in client computer 102 (FIG. 1)and the dynamic pruning program 110 b (FIG. 1) in network server 112(FIG. 1), may be stored on one or more computer-readable tangiblestorage devices 916 for execution by one or more processors 906 via oneor more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 4, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the dynamic pruning program 110 a and 110 b(FIG. 1) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 920, read via the respectiveR/W drive or interface 918 and loaded into the respective hard drive916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 (FIG. 1) and the dynamic pruning program 110 a(FIG. 1) in client computer 102 (FIG. 1) and the dynamic pruning program110 b (FIG. 1) in network server computer 112 (FIG. 1) can be downloadedfrom an external computer (e.g., server) via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 922. From the network adapters(or switch port adaptors) or interfaces 922, the software program 108(FIG. 1) and the dynamic pruning program 110 a (FIG. 1) in clientcomputer 102 (FIG. 1) and the dynamic pruning program 110 b (FIG. 1) innetwork server computer 112 (FIG. 1) are loaded into the respective harddrive 916. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926 andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in tangible storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 (FIG. 5) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and dynamic pruning 96.

Referring now to FIG. 7, shown is a block diagram of an exemplary systemarchitecture 700, including a dynamic pruning program 110 b and anatural language processing system 712, configured to analyze the atleast one determined topic or other electronic communication to identifycontent and to generate related information based on attributes of theat least one determined topic, in accordance with embodiments of thepresent disclosure. In some embodiments, a remote device may submitelectronic documents (such as electronic messages of the socialnetworking system) to be analyzed to the natural language processingsystem 712 which may be housed on a host device. In some embodiments, asecond remote device may submit other electronic content (such ascontent displayed on a social networking system) to be analyzed to thenatural language processing system 712. Such remote devices may eachinclude a client application 708, which may itself involve one or moreentities operable to generate or modify content from a social networkingsystem or other electronic communication that is then dispatched to anatural language processing system 712 via a network 116.

Consistent with various embodiments, the natural language processingsystem 712 may respond to content submissions sent by a clientapplication 708. Specifically, the natural language processing system712 may analyze a received content from a social networking system orother received electronic communication content to identifycharacteristics about the received content (e.g., keywords, events,projects, and/or titles of messages). In some embodiments, the naturallanguage processing system 712 may include a natural language processor714, and data sources 724. The natural language processor 714 may be acomputer module that analyzes the received content. The natural languageprocessor 714 may perform various methods and techniques for analyzingthe received content (e.g., syntactic analysis, semantic analysis,etc.). The natural language processor 714 may be configured to recognizeand analyze any number of natural languages. In some embodiments, thenatural language processor 714 may parse passages of the receivedcontent. Further, the natural language processor 714 may include variousmodules to perform analyses of electronic documents. These modules mayinclude, but are not limited to, a tokenizer 716, a part-of-speech (POS)tagger 718, a semantic relationship identifier 720, and a syntacticrelationship identifier 722.

In some embodiments, the tokenizer 716 may be a computer module thatperforms lexical analysis. The tokenizer 716 may convert a sequence ofcharacters into a sequence of tokens. A token may be a string ofcharacters included in written passage and categorized as a meaningfulsymbol. Further, in some embodiments, the tokenizer 716 may identifyword boundaries in content and break any text passages within thecontent into their component text elements, such as words, multiwordtokens, numbers, and punctuation marks. In some embodiments, thetokenizer 716 may receive a string of characters, identify the lexemesin the string, and categorize them into tokens.

Consistent with various embodiments, the POS tagger 718 may be acomputer module that marks up a word in passages to correspond to aparticular part of speech. The POS tagger 718 may read a passage orother text in natural language and assign a part of speech to each wordor other token. The POS tagger 718 may determine the part of speech towhich a word (or other text element) corresponds based on the definitionof the word and the context of the word. The context of a word may bebased on its relationship with adjacent and related words in a phrase,sentence, or paragraph. In some embodiments, the context of a word maybe dependent on one or more previously analyzed content (e.g., thecontent of message may shed light on the meaning of text elements inrelated message, or content of a first message by a user on an socialnetworking system may shed light on meaning of text elements of a secondmessage by that user on the same or different social networking system).Examples of parts of speech that may be assigned to words include, butare not limited to, nouns, verbs, adjectives, adverbs, and the like.Examples of other part of speech categories that POS tagger 718 mayassign include, but are not limited to, comparative or superlativeadverbs, wh-adverbs, conjunctions, determiners, negative particles,possessive markers, prepositions, wh-pronouns, and the like. In someembodiments, the POS tagger 718 may tag or otherwise annotate tokens ofa passage with part of speech categories. In some embodiments, the POStagger 718 may tag tokens or words of a passage to be parsed by thenatural language processing system 712.

In some embodiments, the semantic relationship identifier 720 may be acomputer module that may be configured to identify semanticrelationships of recognized text elements (e.g., words, phrases) inreceived content. In some embodiments, the semantic relationshipidentifier 720 may determine functional dependencies between entitiesand other semantic relationships.

Consistent with various embodiments, the syntactic relationshipidentifier 722 may be a computer module that may be configured toidentify syntactic relationships in a passage composed of tokens. Thesyntactic relationship identifier 722 may determine the grammaticalstructure of sentences such as, for example, which groups of words areassociated as phrases and which word is the subject or object of a verb.The syntactic relationship identifier 722 may conform to formal grammar.

In some embodiments, the natural language processor 714 may be acomputer module that may parse received content and generatecorresponding data structures for one or more portions of the receivedcontent. For example, in response to receiving correspondence from asocial networking system at the natural language processing system 712,the natural language processor 714 may output parsed text elements fromthe correspondence as data structures. In some embodiments, a parsedtext element may be represented in the form of a parse tree or othergraph structure. To generate the parsed text element, the naturallanguage processor 714 may trigger computer modules 716-722.

In some embodiments, the output of natural language processor 714 (e.g.,ingested content) may be stored within data sources 724, such as corpus726. As used herein, a corpus may refer to one or more data sources,such as the data sources 724 of FIG. 7. In some embodiments, the datasources 724 may include data warehouses, corpora, data models, anddocument repositories. In some embodiments, the corpus 726 may be arelational database.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer program product for dynamicallyprocessing information in an activity stream based on a uniqueness and arelationship criteria, the computer program product comprising anon-transitory computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to perform a method comprising: analyzing, utilizing naturallanguage processing, correspondences in a social networking system todetermine at least one topic, wherein the social networking system is anews feed; associating an activity stream with the at least onedetermined topic; identifying a target audience for the activity stream,wherein identifying the target audience includes parsing the at leastone determined topic and generating a parse tree for the at least onedetermined topic; analyzing the activity stream according to auniqueness score and a relationship criteria to form an assessment,wherein the relationship criteria is a social relationship between thetarget audience and the at least one determined topic, wherein theanalyzing the activity stream according to the uniqueness and therelationship criteria to form the assessment further comprises:generating a knowledge graph based on the at least one determined topicand the associated activity stream, wherein the knowledge graph includesa first plurality of nodes representing a first plurality of content anda second plurality of nodes representing a second plurality of content,wherein edges between the first plurality of nodes and the secondplurality of nodes represent links between the first plurality ofcontent and the second plurality of content; and calculating auniqueness score that is a numerical value for each node in the secondplurality of nodes based on a number of edges between the firstplurality of nodes and the second plurality of nodes, and furtheraccording to the uniqueness and the relationship criteria, wherein theuniqueness between two nodes is defined by the distance of a linkbetween the two nodes, and wherein the first plurality of nodes is theat least one determined topic and the second plurality of nodes isunselected messages; performing the assessment that is a groupedactivity stream including selected messages, the target audience, andremaining messages; comparing the assessment to a predetermined actioncriteria; performing an action in response to determining the assessmentsatisfies the predetermined action criteria, wherein the performedaction is removing a portion of the activity stream from the socialnetworking system; archiving the portion of the activity stream into oneor more databases each at a remote location according to the at leastone determined topic; and presenting an organizational update of theactivity stream to an end user.